Responsible investment into the global mining industry – a spatial analysis

Responsible investment into the global mining industry – a spatial analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Analysis Responsible investment into the global mining industry – a spatial analysis Éléonore Lèbre, Nevenka Bulovic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5954459/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Responsible investment, namely the integration of Environmental, Social and Governance data into investment decisions, is now a mainstream practice in the finance sector. However, questions remain on how investors make decisions based on this type of data and whether these decisions lead to improvements in social and environmental outcomes on the ground. This paper analyses investment into the global mining sector through the institutional ownership records of a global dataset of 37,434 mining properties and 9,843 mining companies. The study reveals that 626 “responsible” investors, namely institutional investors that signed the Principles for Responsible Investment, own stakes in 1,247 mining companies and 7,583 mining properties. These investors prefer to invest in companies with higher Environmental, Social and Governance performance scores, with an average score 13% higher than the industry average. Tailings governance, corruption risk, and proximity to protected areas receive consideration from responsible investors, while water scarcity and proximity to Indigenous land do not. Contrary to expectations, responsible investors invest 23% more in coal mining properties than the global average. We surmise that current investment approaches are unlikely to contribute to significant on-the-ground improvement in practice. Earth and environmental sciences/Environmental social sciences/Sustainability Scientific community and society/Business and industry/Business Scientific community and society/Geography Figures Figure 1 Figure 2 Introduction Responsible investment, namely the integration of Environmental, Social and Governance (ESG) factors into investment decisions, is a fast-growing practice in the finance sector. Founded in 2005, the Principles for Responsible Investment (PRI) now gather signatories that cover 50% of managed capital globally 1 . A Deloitte survey estimated that the practice of ESG integration has been adopted by 83% of investors 2 , although a lower percentage (34%) admit to considering ESG factors regularly, showing a potential disconnect between commitments and actions. The practice is motivated by a consensus among investors that ESG performance and financial performance are positively interlinked. Several seminal studies 3 – 6 have found a causal relationship between ESG and financial performance, however, this relationship is dependent on the ESG topic being considered, the data used to measure this topic, and the focus geography 7 . Therefore, questions remain on whether and how responsible investors, aka PRI signatories, make decisions based on ESG data. What kind of decisions are made? What kind of data is being used? A lot about the practice remains hidden in in-house proprietary data systems and internal processes. There is evidence 8 that ESG integration is happening, and that it has an impact on investees, i.e. that responsible investing leads to positive sustainability outcomes across industry sectors. However, there are major data quality issues 2 , 9 , 10 , which limit decision accuracy and impact, and lead to certain ESG factors being better integrated than others 11 . Most of the growth in ESG integration has occurred over the past five years, and there remains substantial potential for improvement. A major investor intervention occurred in the mining sector after the Brumadinho tailings dam failure that killed 272 people in 2019. The intervention had an immediate impact on the sector, leading to a new global standard on tailings management launched in 2020 and the creation, in 2022, of the Global Investor Commission on Mining (GICM) 2030. Mining is one of the lowest-performing and highest-risk sectors according to several ESG rating providers 8 , 12 , 13 . At the same time, the mining industry is also a supplier of minerals and metals needed in the energy transition, making it essential to global decarbonisation 14 . In this context, the finance sector can exert a high influence in shaping ESG performance in mining, yet it must also navigate a complex and challenging landscape. This paper reverse-engineers investors' decision-making process to identify the factors they consider when investing in the mining sector. To achieve this, we analysed the investor records of a dataset of 37,434 geolocated mining properties owned by 9,843 mining companies 15 . 7,583 properties (1,247 companies) are receiving investment from PRI signatories, and 3,466 (447 companies) from GICM 2030 supporters - a subset of PRI signatories actively involved in the mining sector. We applied a set of ESG metrics from S&P Global 13 and the Metals and Mining Sustainability Accounting Standard 16 , 17 , otherwise known as the SASB standard and principally used by investors 18 (see Supplementary Table 1 for the list of topics and metrics in the SASB standard). The paper finds evidence that ESG factors may be influencing investment decisions, but questions whether these decisions are leading to positive sustainability outcomes across the sector. Results Responsible investors’ stakes in the global mining sector The information gap around mining asset ownership is substantial. This is consistent with the literature documenting data gaps across the global mining sector 10 , 19 . Of 37,434 geolocated mining assets, 14,886 (40%) have no ownership record (i.e., the companies that own the assets) and no institutional investor record (i.e., the finance organisations that own the companies), and another 12,527 (33%) have an ownership record but no institutional investor record (Supplementary Figs. 1 & 2). These 12,527 assets are typically owned by private companies, governments, or public companies exclusively owned by individuals. The remaining 10,021 assets (27%) have investor records (Supplementary Fig. 3). A total of 3,247 different institutional investors are listed as having some equity in these assets, out of which 626 are PRI signatories, which corresponds to 12% of the 5,348 PRI signatories globally (as of March 2024). Overall, 7,583 assets are partly owned by PRI investors (Fig. 1 a, b), and 9,506 by non-PRI investors, with 7,068 assets owned by both types of institutional investors (and 2,953 owned exclusively by one type of investor). While non-PRI investors own 23% more assets, their ownership percentage is often lower than PRI investors (Fig. 1 b). Taking these percentages into account, PRI investors ownership stake is 28% more than non-PRI investors, although there are almost four times more non-PRI investors than PRI investors in the dataset. This means a small proportion of PRI signatories are investing into the mining sector, but those that do invest heavily compared to other institutional investors. Figure 1 d shows that 12 of the top 15 investors by number of assets are PRI signatories (See Supplementary Table 2 for the list of the top 100 largest institutional investors by mining portfolio size). 31 institutional investors, about 5% of PRI investors owning mining properties, are GICM 2030 supporters. Because they are fewer, these investors cumulatively own smaller stakes in mining properties compared to the whole PRI cohort (Fig. 1 b). However, this small cohort is invested in a large number of properties – 3,466, i.e. about half of PRI properties. Konwave is the GICM investor with the largest mining portfolio (1,294 assets), followed by Ninety One (757) and Québec’s Caisse de Dépôt et Placement (716) (See Supplementary Table 3 for the list of GICM 2030 supporters owning mining properties). This indicates that GICM 2030 supporters have a particular interest in the mining sector compared to other PRI investors. However, GICM investors do not appear to be more active shareholders compared to other investors based on the PRI shareholder resolution database (PRI, 2024b). Proposing and voting resolutions are ways through which investors can influence companies on ESG matters. Only one resolution involving GICM investors was found: Ninety One and Rathbones voting against Glencore's 2024 Climate Action Plan, judging it not ambitious enough. For further analysis of shareholder resolutions, see Supplementary Text 1. Geographic distributions of PRI and non-PRI properties exhibit some notable differences in top mining countries. PRI investors are significantly less present in China, Russia, India, the Philippines, Mexico, and Kyrgyzstan (Fig. 1 c). This could be reflective of geopolitical alliances and the current move of some major western economies to diversify mineral production away from China and Russia 20 , 21 . This is also reflective of the general geography of the PRI movement, with only 3% of all PRI signatories headquartered in China, and only one signatory headquartered in Russia. Compared to other PRI investors, GICM investors are more present in Canada, Sweden and Finland, but also Russia, Papua New Guinea and Bolivia (Supplementary Fig. 4). They appear to avoid countries like China, India and the Democratic Republic of Congo to a larger extent than other PRI investors. ESG considerations in mining asset ownership ESG scores from S&P Global were extracted for the mining companies in our dataset. See Methods for how S&P ESG scores are built. Scores were available only for 358 (out of 9,843) mining companies, together owning 4,154 mining properties, i.e. 11% of the dataset. Analysis of this sample reveals that mining portfolios of PRI and GICM 2030 investors exhibit higher ESG score – 13 and 18% higher, respectively – than the global average. Figure 2 a shows that companies owned more than 10% by PRI investors have noticeably higher ESG scores, and so do companies owned by GICM investors (all percentages included). Spearman rank correlation coefficients are 0.401 for PRI and 0.391 for GICM investors (Fig. 2 b). In comparison, companies financed by non-PRI investors have ESG scores 5% higher than the global average. PRI and GICM portfolios exhibit noteworthy specificities, with PRI investors displaying a preference for large mining companies while GICM investors have a more balanced portfolio. The GICM cohort is more invested than the PRI cohort in exploration projects (Supplementary Fig. 5), and the exploration sector being dominated by junior companies largely explains the difference between PRI and GICM portfolios. ESG scores are correlated to company size (measured in number of properties owned), as large companies have higher incentives (visibility and reputation) as well as capability and resources to improve their ESG performance 22 . When discounting the size effect, correlations between PRI/GICM percentages and ESG scores are lower but remain strong – with coefficients of 0.347 for PRI and 0.357 for GICM (Supplementary Table 4). These results indicate that PRI and GICM investors are considering ESG metrics in investment decisions, to a larger extent than non-PRI investors. To complement S&P ESG scores and expand the analysis to the full set of 37,434 mining properties and 9,843 mining companies, we created eight individual ESG metrics that were applicable to the entire dataset: coal properties, tailings management, water scarcity, corruption risk, protected areas, threatened species, Indigenous land, and conflict areas. Apart from tailings management, which measures a company’s commitment to implementing the Global Industry Standard on Tailings Management (GISTM) and is therefore a company-wide metric, all selected metrics are property-specific, meaning values vary across properties owned by the same company. They relate to either the characteristic of a property (e.g. whether the property extracts coal) or its location (e.g. whether a property is in a protected area). These eight metrics were selected to align with the ESG topics covered by the SASB standard. The SASB standard defines these topics as “sustainability-related risks and opportunities that could reasonably be expected to affect [a mining company’s] cash flows, its access to finance or cost of capital” 17 . For this analysis, metrics were defined to measure a risk (e.g. absence of commitment to tailings management) rather than an opportunity (see Methods and Supplementary Table 1 for how metrics were calculated). We find that GICM investors avoid coal properties, with an investment proportion of 4% while coal properties make up 14% of the dataset. Conversely, both PRI and non-PRI investors invest in coal properties in proportions higher than the global average (Figs. 2 c,d). Results are statistically significant. This is a surprising finding as decarbonization is the ESG topic that has received the most attention from responsible investors, in the mining sector and elsewhere 11 , 23 . Ownership of coal properties has been the object of scrutiny from investors. BlackRock’s announcement that it will no longer invest in companies that generate more than 25% of their revenue from thermal coal production incentivised large mining companies like BHP, Rio Tinto and South32 to divest their coal properties 24 , 25 . Findings indicate that these publicized events, and the small cohort of GICM investors, may be the exception rather than the norm across the global mining sector. Noting, however, that the proportion of coal properties is only one decarbonization metric among others and it is possible that investors are acting in other ways. PRI and GICM investors invest preferably in companies committed to implementing the GISTM. While GISTM-committed companies only make up 1% of all mining companies, they make up 20% of PRI and GICM investments (Figs. 2 c,d). Non-PRI investors also appear to consider this topic, but to a lesser extent. 10% of non-PRI investments target GISTM-committed companies. Tailings management gained visibility following the mining sector’s credibility crisis 26 , triggered by the 2019 catastrophic tailings dam failure in Brumadinho, Brazil. Shortly after the event, a coalition of 100 investors issued a call to action, pressing the mining sector to address this systemic safety issue and have monitored progress ever since. The Church of England Pension Board’s (the institution that led the Investor Mining and Tailings Safety Initiative) official list of GISTM-committed companies is publicly available on their website 27 and represents an easy way for investors to filter out non-committed companies. Noting that most GISTM-committed companies are large and therefore company size may be a confounding variable. Corruption appears to receive some consideration from PRI, non-PRI, and GICM investors. Statistical tests indicate non-PRI investors consider corruption risk the most (see Figs. 2 c,d). While there is a 19% decrease in corruption risk in the weighted average for GICM investors, the trend is not statistically significant. Mining properties receive a corruption risk score based on the country in which they are located, and which corresponds to the country’s Corruption Perception Index 28 score (see Methods). The average proportion of properties located nearby a violent conflict is 16% and 19% lower in PRI and GICM portfolios respectively compared to the global average. However, logistic regression test only shows weak correlation for conflict, and the chi-square test returns no statistical significance. Corruption and conflict areas are both measures of a country’s governance, and the countries in which a company operates has long been a factor influencing credit ratings 29 , 30 , i.e. a company's creditworthiness and therefore its ability to repay its debt. Our results confirm that institutional investors generally recognise the financial implications of corruption risk for business. Conversely, local conflict may not be a broadly accepted governance measure. The two biodiversity metrics, threatened species and protected areas, appear to receive some consideration from investors. The GICM portfolio’s exposure to threatened species is 22% lower than the global average, meaning mining properties that receive GICM investment are less likely to be in areas with identified threatened species. The negative correlations between threatened species richness and GICM and non-PRI ownership are weak but significant. Conversely, GICM investors do not seem to consider protected areas in their portfolio decisions, while PRI and non-PRI investors do (and the relationship is statistically significant). A recent position statement from a group of major mining companies commits to “respecting legally designated protected areas” 31 . Movement in this space appears to be reflected in investment flows; although the observed correlations may also be due to PRI and non-PRI investors preferring major mining companies, regardless of their biodiversity commitments. Finally, GICM investors’ portfolios appear to be exposed to levels of water scarcity 22% lower than the global average. This difference is significant according to the logistic regression test, but not the chi-square test. Analysis of mining properties’ overlap with Indigenous land reveals no statistical relationship, indicating that investors do not consider this topic. Discussion Overall, the analysis yielded two key insights. Firstly, responsible investors consider ESG metrics in their investment decisions. The comparison of PRI and non-PRI portfolios suggests significant differences in practices between the two investor cohorts. PRI signatories state that their primary motivations for joining the PRI are “long-term value” and “risk management” 11 , indicating that these investors see ESG as financially material and are acting accordingly. It should be noted that the Principles for Responsible Investment are not a constraining framework and allow signatories to exercise their own judgement over which ESG topics are important to them 11 . Secondly, PRI signatories have a significant presence across the global mining sector. This contrasts with stated concerns about the sector being systematically underweighted and screened out by responsible investors due to its poor ESG credentials 32 . Negative screening, a practice where investors exclude or divest from companies or assets that do not meet certain ESG criteria, has been shown to be counterproductive, as it fails to encourage improvement in the performance of the targeted firms 33 . A potential reason why we did not find evidence of negative screening could be that large investors, who offer a variety of funds to their clients, exclude mining companies from their ‘sustainable’ funds (funds that consider ESG criteria) but include them in their ‘traditional’ funds. This may also explain why we did not observe a screen-out of coal properties in the PRI cohort, at the exception of GICM investors. The fact that PRI and GICM investors are involved in the mining sector and see the importance of ESG in their investment decisions is positive news. Through their financial stakes in companies and influence on boards of directors, investors can be critical agents of change 32 . The simple growth of the PRI initiative sends a signal to companies that encourages them to improve their ESG performance 12 . Despite this, it is not clear whether current investment strategies do result in tangible improvement in social and environmental outcomes on the ground. Our analysis does not allow reaching a conclusion on this matter. However, we can provide initial reflections based on available information and literature. Our analysis suggests PRI and GICM investors rely on aggregated ESG scores. This is positive and yet problematic as these scores are widely acknowledged, even amongst the investment community, as being poor performance measures 2 , 34 . One reason is that a single score is too simplistic and hides variations in performance across topics. Another reason is that ESG scores typically measure commitments rather than actions, and the presence of corporate policies rather than their effective implementation on the ground. The strong correlations found between PRI investment and a company’s commitment to the global tailings standard is an example of a focus on pledges rather than performance. While standards are needed components of corporate governance, there is a risk that investors only engage superficially with the mining sector and that this does not result in meaningful social and environmental outcomes in extractive locations. Responsible investors may be mainly applying a one-size-fits-all approach, rather than a specialised approach that recognises the diversity of ESG topics and operating contexts. The analysis of shareholder resolutions (Supplementary Text 1) suggests that specialised practices, such as active stewardship where investors directly engage with boards of directors on ESG matters, are rare 32 . One nuance to highlight, however, is the observed trend in the GICM cohort, who exhibits a more sophisticated investment approach that considers some situated risks (i.e. risks based on a property’s location), particularly water scarcity and threatened species. Overall, our findings align with responsible investment studies outside the mining sector. These studies have found that ESG integration have limited impact on social and environmental outcomes 22 , 35 – 38 , meaning that responsible investment may not have delivered on its promises. The Cambridge Institute for Sustainability Leadership finds that ESG and the boom in responsible funds have not only failed to deliver but have been counter-productive at times as they give the impression of progress while having “no realistic prospect of doing enough” 39 . A significant proportion of investors genuinely aim to invest in support of sustainability objectives, but do not have appropriate mandates, frameworks, tools, or data, to do so effectively. Research efforts need to be dedicated to supporting responsible investors in their practice. Furthermore, there is a need for mining companies to drive improvement in investor relations, by communicating more meaningfully about their ESG performance and building investor support on the ESG initiatives that truly result in positive outcomes. Methods Overall data quality. The S&P Capital IQ Pro database forms the basis of nearly all global assessments published on the mining sector 19 . Its geolocated mining property dataset provides a rich source of information for spatial analysis. However, this database is incomplete and sometimes inaccurate. Data quality issues are also found in ESG datasets. While this is a general limitation, inaccuracies linked to property locations and ESG metrics are not expected to affect the main findings and conclusions of the study. The study’s focus is on understanding how investment decisions are made and with what data, not on identifying what ESG risks are indeed present in a given mining location. The study assumes that investors have access to the same (or similar) incomplete and inaccurate data and make decisions based on this data. However, other data quality issues may affect the results and are noted in the rest of this section, as well as in the main text where appropriate. Ownership information. Ownership records were extracted for the 37,434 mining properties listed in the S&P database. Removal of duplicates returned a list of 9,843 mining companies (public or private). For these companies, institutional investor equity percentages were extracted for the 30 largest investors by equity percentage. The S&P database’s screener function does not allow extraction of more than 30 investors. This limitation affects 473 companies that have more than 30 investors. Removal of duplicates returned a list of 3,247 institutional investors with stakes in mining companies. The S&P list of investors was matched against the PRI list of 5,348 signatories and the GICM 2030 list of 82 supporters. This step required manual checks as investor names in the S&P database were often different from names in the PRI and GICM lists. For instance, “Brookfield Corp.” in the S&P database was matched with “Brookfield Corporation” in the PRI list. When an investor from the S&P list was a 100% subsidiary of a larger PRI signatory, this investor was identified as a PRI signatory, while the reverse was not. This results in a conservative list of PRI investors. Because of time constraints, not all 3,247 investors could be manually checked. Investors that had two or fewer mining companies, five or fewer mining assets, and who’s equity position was 11th or lower (i.e. the investor was not in the top 10 in equity percentage), were not checked. This resulted in a small number of investors being left out. Overall, 92% of investors were identified as being either “PRI” or “non-PRI”. S&P Global ESG scores. These scores, calculated by S&P for certain companies, measure companies’ performance on, and management of, material ESG issues. An issue is considered material if it presents a significant risk, opportunity or impact on i) society or the environment, and ii) on a company’s long-term financial performance. The scores rely on data collected from corporate disclosures, media and other public stakeholder information. They are measured on a scale of 0–100 where 100 represents the best performance. The full methodology for building the ESG score is publicly available 40 , but the scores themselves are available via subscription. Individual ESG risk metrics. Except for tailings management, which is a company-level metric, individual ESG metrics are calculated at the property-level. Six of the eight individual ESG metrics, namely protected areas, coal properties, tailings management, water scarcity, conflict areas and Indigenous land, are categorical and binary, i.e. they represent the presence or absence of a condition or criterion (for instance, a property is either nearby a conflict area or not; a company is either GISTM-committed or not). For these metrics, mining properties are thus either attributed a 1 or a 0, where 1 corresponds to the presence of risk. The remaining two metrics, corruption and threatened species, are continuous metrics and are normalised for properties to receive a value between 0 and 1, where 1 corresponds to the highest level of risk. Metrics are defined as follows: (1) The tailings management metric records when a company is not listed as being GISTM-committed. The list of committed companies is available on the Church of England Pension Board’s website 27 . (2) The coal properties metric records whether a property’s primary targeted commodity is coal. Such a property receives a value of 1, while others receive a value of 0. Commodity information at the property level is sourced from the S&P Global database 13 . (3) The corruption metric is calculated using Transparency International’s Corruption Perceptions Index 28 . Properties located in a particular country are assigned the index value of that country. Corruption Perception Index values were inversed so that higher corruption risks were associated with higher metric scores. (4) The conflict areas metric measures whether a mining property is located nearby (less than 10 km) a violent conflict using the Uppsala Conflict Data Program’s Georeferenced Event Dataset 41 . (5) Water scarcity uses the World Resources Institute’s Aqueduct Water Risk Framework 42 . It overlays mining property locations with the Aqueduct’s Baseline Water Stress map and assesses whether a property is in an area of high or very high water stress. (6) The threatened species metric uses the International Union for Conservation of Nature’s Red List of Threatened Species 43 and overlays property locations with species locations. (7) The protected areas metric uses the International Union for Conservation of Nature’s World Database of Protected Areas 44 and analyses whether a property falls in or nearby (less than 5 km) a protected area. (8) The Indigenous land metric uses Garnett et al.’s 45 Indigenous Peoples land map and analyses whether a property falls in or nearby (less than 5 km) Indigenous Peoples land. Individual metrics were selected to align with the SASB standard on mining and metals (see Supplementary Table 1). SASB standards are curated by the IFRS Foundation since 2001 and are required for use by more than 140 jurisdictions 16 . SASB standards are designed to streamline the disclosure and collection of comparable and standardised data tailored for investors to make informed decisions. The IFRS states that “because they are industry-based, metric-driven and focused on the risks and opportunities most likely to affect cash flows, access to finance and cost of capital, SASB Standards enable integration of sustainability considerations into investment and stewardship decisions across global portfolios and asset classes” 18 . The PRI association promotes the use of the SASB standards 46 . It should be noted that the level of alignment between the metrics used in the study and the standard varies across metrics (Supplementary Table 1). Corruption risk is the metric most closely aligned, as the standard explicitly mentions the Corruption Perception Index. The threatened species and protected area metrics are also fairly aligned, as the datasets used to measure these factors are widely recognised as the main points of reference for global assessments. This is not the case for water scarcity and conflict areas, for which several reputable datasets exist, and for Indigenous land, where the only global spatial dataset available is not in open access. Furthermore, maps of Indigenous land occupation or management are often contentious and subject to definitional issues 45 . Tailings management and coal properties are the least aligned metrics as they overly simplify the content of the standard. However, they represent meaningful proxies. Differences in alignment may influence some of our findings. Finally, a general source of misalignment is that SASB standard typically requires to measure the percentage of production or the percentage of reserves e.g. in high corruption-risk countries, or in protected areas, whereas our study measures percentage of properties. We do not rely on production or reserves data for this study due to their incompleteness and inaccuracy. Using this data would risk compounding inaccuracies and would result in the sample size being considerably reduced, as it would exclude many properties that do not have production or reserves data. Weighted average relative difference. For each individual ESG metric, this value is calculated using the following equation: $$\:\frac{{A}_{x}-B}{({A}_{x}+B)/2}$$ Where \(\:{A}_{x}\) is the weighted average for investor type \(\:x\) : $$\:{A}_{x}=\:\frac{\sum\:_{i=1}^{n}{ESG}_{i}\times\:{Ownership\%}_{i,x}}{{{\sum\:}_{i=1}^{n}Ownership\%}_{i,x}}$$ And \(\:B\) is the global average of the ESG metric: $$\:B=\frac{{\sum\:}_{i=1}^{n}{ESG}_{i}}{n}$$ Where \(\:{ESG}_{i}\) is the metric value at mining property \(\:i\) , and \(\:{Ownership\%}_{i,x}\) is the aggregated ownership percentage of investor type \(\:x\) at mining property \(\:i\) . For tailings management, which is calculated at the company level, \(\:n=\:\text{9,843}\) , i.e. the total number of mining companies. For coal properties, protected areas, conflict areas, and Indigenous land, \(\:n=\text{37,434}\) , i.e. the total number of mining properties. For corruption risk, water scarcity and threatened species, a small number of properties fall in areas of “no data” (98, 286, and 757, respectively) and were therefore removed from the calculation. Hence, for these three metrics, \(\:n<\text{37,434}\) . Statistical tests. Statistical tests were undertaken to evaluate the robustness of the results. See Supplementary Tables 5–8 for all test results. For categorical metrics (protected areas, coal properties, tailings management, water scarcity, conflict areas and Indigenous land), the first step was to use the “chi-square goodness of fit test” to test whether the relative occurrence of an ESG risk at properties owned by a certain investor type (regardless of the ownership percentage) was statistically different to the general occurrence based on p-values. The second step was to see whether occurrence of a certain ESG factor was related to the percentage of ownership of different investor types using logistic regression, where the parameter value indicates the direction and magnitude of the relationship while the parameter p-value measures statistical significance. For instance, logistic regression tests whether the likelihood of a coal asset occurring is related to the PRI ownership percentage. Separate tests were designed for continuous metrics, i.e. threatened species and corruption. The first test was to check whether the frequency distribution of these metrics at properties owned by certain investor types (regardless of the ownership percentage) was significantly different from their distribution across all mining properties. Assuming that institutional investors would prefer mining properties with lower ESG risks, an “Unpaired Mann-Whitney-Wilcoxon U test” was used to test whether the distribution of properties owned by a particular investor type was significantly lower than that of all mining properties. This test serves a similar purpose to chi-squared test but for continuous data. Results are expressed in terms of p-values (in Supplementary Table 7). The second test was to see whether the value of a certain ESG metric was related to percentage of ownership of a particular investor type using Spearman correlations, whereby p-values were derived with t-tests. Spearman correlations were used because of the strong non-normality of ESG metrics and ownership percentage distributions. This test was performed for two property cohorts: one that contained all mining properties, and one that contained only mining properties that had some ownership by the specific institutional investor type being considered (e.g. for PRI investors, only properties with a non-zero PRI ownership percentage were considered). Due to the high number of properties with 0% ownership by a specific investor type, this second assessment was included to better understand the distribution of investor funds in selected properties as opposed to all properties. References PRI. PRI Strategy Plan 2024-27. (2024) Deloitte (2024) Investor trust in sustainability data - An opportunity for corporate leaders Edmans A (2012) The Link Between Job Satisfaction and Firm Value, With Implications for Corporate Social Responsibility. Acad Manage Perspect 26:1–19. 10.5465/amp.2012.0046 Eccles RG, Ioannou I, Serafeim G (2014) The Impact of Corporate Sustainability on Organizational Processes and Performance. 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J Clean Prod 474:143563. https://doi.org/10.1016/j.jclepro.2024.143563 PRI, Inside (2022) PRI data: asset owner actionPrinciples for Responsible Investment, London, UK Innis S, Kunz NC (2020) The role of institutional mining investors in driving responsible tailings management. Extractive Industries Soc 7. https://doi.org/10.1016/j.exis.2020.10.014 S&P Global. in S&P Capital IQ Pro (Thomson Reuters, New York, 2024). Lèbre É et al (2020) The social and environmental complexities of extracting energy transition metals. Nat Commun 11:4823. 10.1038/s41467-020-18661-9 Lèbre É, Bulovic N, Mining (2025) responsible investment and ESG: a global spatial dataset of mining companies and properties. https://doi.org/10.48610/2c8cbf6 (Centre for Social Responsibility in Mining, The University of Queensland, Brisbane, QLD, Australia IFRS (2024) Who we are , https://www.ifrs.org/about-us/who-we-are/ IFRS (2023) Metals & Mining Sustainability Accounting Standard. in Industry Standard | Version 2023-12 . The IFRS Foundation, London, UK IFRS (2024) Why investors use SASB Standards , < https://sasb.ifrs.org/investor-use/ Maus V, Werner TT (2024) Impacts for half of the world’s mining areas are undocumented. Nature 625:26–29 Jackson L (2024) EU asks Australia to make foreign investment easier, eyes critical minerals. in Reuters Moors C (2022) Chinese investments in US mining face 'uphill battle'. in S&P Capital IQ Pro Commodity Insights Drempetic S, Klein C, Zwergel B (2020) The influence of firm size on the ESG score: Corporate sustainability ratings under review. J Bus Ethics 167:333–360 Bloomberg News. Goldman has a stock model that’s challenging ESG assumptions. in Mining.com (2024) Secrett R, Rowson G (2024) Divestment: a short-sighted solution for responsible coal mine closure. in Bettercoal MEU (2024) South32 joins major international peers in divesting from coal. in Min Energy Union Hopkins A, Kemp D (2021) Credibility crisis: Brumadinho and the politics of industry reform. CCH Australia IMTSI. Tailings Response Database [version updated 26.1.24] (ed Investor Mining and Tailings Safety Initiative) (2024) Transparency International (2023) Corruption Perceptions Index (ed Transparency International). Berlin, Germany. https://www.transparency.org/en/cpi/2020 Hasan R, Ashfaq M (2021) Corruption and its diverse effect on credit risk: global evidence. Future Bus J 7:18 PRS Group (2025) Our Products - The International Country Risk Guide (ICRG) , https://www.prsgroup.com/explore-our-products/icrg/ ICMM. Nature - Position Statement (2024) GICM (2030) Landscape Report - The Role of Investors in Realising an Environmentally and Socially Responsible Mining Industry. (Global Investor Commission on Mining 2030, London, UK, 2024) Hartzmark SM, Shue K (2022) Counterproductive sustainable investing: The impact elasticity of brown and green firms. Preprint at http://dx.doi.org/10.2139/ssrn.4359282 Kotsantonis S, Serafeim G (2019) Four things no one will tell you about ESG data. J Appl Corp Finance 31:50–58 Edmans A, Gosling T, Jenter D (2024) Sustainable Investing: Evidence From the Field. Preprint at http://dx.doi.org/10.2139/ssrn.4963062 Heath D, Macciocchi D, Michaely R, Ringgenberg C (2023) M. Does socially responsible investing change firm behavior? Rev Financ 27:2057–2083 Kölbel JF, Heeb F, Paetzold F, Busch T (2020) Can sustainable investing save the world? Reviewing the mechanisms of investor impact. Organ Environ 33:554–574 Gosling T (2024) Universal Owners and Climate Change. J Financial Regul. 10.1093/jfr/fjae010 Hooper L, Gilding P (2024) Survival of the Fittest: From ESG to Competitive Sustainability. Cambridge Institute for Sustainability Leadership, Cambridge, UK S&P Global. S&P Global Sustainable1: S&P Global ESG Scores Methodology. (2024). Sundberg R, Melander E (2013) Introducing the UCDP georeferenced event dataset. J Peace Res 50:523–532 Gassert F, Landis M, Luck M, Reig P, Shiao T (2013) Aqueduct Metadata Document - Aqueduct Global Maps 2.0. World Resources Institute, Washington DC, United States IUCN (2023) Species Richness 2023 (Amphibians, Birds, Mammals and Reptiles) - Threatened Species. (ed IUCN Red List) https://www.iucnredlist.org/resources/other-spatial-downloads#SR_2023 UNEP-WCMC. in (2024) Protected Planet: The World Database on Protected Areas (WDPA) www.protectedplanet.net (UN Environment Programme. World Conservation Monitoring Centre and International Union for Conservation of Nature, Cambridge, UK Garnett ST et al (2018) A spatial overview of the global importance of Indigenous lands for conservation. Nat Sustain 1:369–374. 10.1038/s41893-018-0100-6 PRI. PRI supports progress on IFRS Sustainability Standards (2023) Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5954459","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Analysis","associatedPublications":[],"authors":[{"id":414177662,"identity":"ee5be65e-292b-481c-bc32-7f2af0e9f831","order_by":0,"name":"Éléonore Lèbre","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFAC5gZmICkHZvMQp4URrMWYdC2JDURr4WdgbHxcuMcmfcPxBMYHb9sY5A0OENAi2cDYbDzjWVruhjMPmA3ntjEYbiCkxeAAY5s0z4HDuRtuJLBJ87YxMBLUYn+Asf03z4H/6QY3Eth/A7XYE7aFgbGNmefAgQSgFjZmoJZEglokDjM2S884kGw488zDZsk55ySSZxLSwt/efPBzwQE7eb7jyQc/vCmzse0jpIWBGc4CR40EIfUoIIEk1aNgFIyCUTCCAACce0EcyuVN+gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5159-3446","institution":"University of Queensland","correspondingAuthor":true,"prefix":"","firstName":"Éléonore","middleName":"","lastName":"Lèbre","suffix":""},{"id":414177663,"identity":"93a0ff9a-d031-4e66-b3ee-b79905d53514","order_by":1,"name":"Nevenka Bulovic","email":"","orcid":"https://orcid.org/0000-0002-6981-2395","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Nevenka","middleName":"","lastName":"Bulovic","suffix":""}],"badges":[],"createdAt":"2025-02-04 02:45:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5954459/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5954459/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79436062,"identity":"988de8b8-0402-4ba4-9162-c7a266c833c8","added_by":"auto","created_at":"2025-03-28 12:00:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOwnership of mining properties by institutional investors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Spatial distribution of PRI-owned assets. \u003cstrong\u003eb\u003c/strong\u003e Percentage of ownership in an asset. Comparing distribution in ownership stakes for institutional investors, non-PRI institutional investors, PRI institutional investors, and GICM 2030 investors. \u003cstrong\u003ec\u003c/strong\u003e Relative country preferences of PRI and non-PRI investors in the top 40 mining countries. Negative values mean PRI investors are more present in the country than non-PRI investors. Positive values mean non-PRI investors are more present. \u003cstrong\u003ed\u003c/strong\u003e Top 15 institutional investors by number of mining properties.\u003c/p\u003e","description":"","filename":"Onlinefloatimage19.png","url":"https://assets-eu.researchsquare.com/files/rs-5954459/v1/aeaa5ce92bdbd91a42fe2bd7.png"},{"id":79436493,"identity":"4558869a-f582-4a1c-81d2-414bb866ab33","added_by":"auto","created_at":"2025-03-28 12:08:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between ESG scores and responsible investment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Aggregated company-level ESG score distribution by investor type, where higher scores represent better performance. \u003cstrong\u003eb\u003c/strong\u003e Spearman rank correlation coefficients between ownership percentages, aggregated ESG scores and company size (size expressed in number of mining properties owned). \u003cstrong\u003ec\u003c/strong\u003e Relative difference between the ownership percentage-weighted average for a particular investor type and the world average; and statistical test summary across the eight individual ESG metrics. Negative values in weighted average relative difference correspond to a reduction in ESG risk, i.e. the investor type is performing better than the global average. \u003cstrong\u003ed\u003c/strong\u003e accompanying key.\u003c/p\u003e","description":"","filename":"Onlinefloatimage28.png","url":"https://assets-eu.researchsquare.com/files/rs-5954459/v1/daa7107921063ae2d493d7ac.png"},{"id":79437121,"identity":"6bdf6546-2e35-4d76-bce7-ce99907c1637","added_by":"auto","created_at":"2025-03-28 12:16:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677614,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5954459/v1/d3fd03aa-bc0f-4501-a8b1-07de6d91a3c0.pdf"},{"id":79436059,"identity":"a472e00d-8da8-4613-a83f-1c405d5276d5","added_by":"auto","created_at":"2025-03-28 12:00:30","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2078731,"visible":true,"origin":"","legend":"","description":"","filename":"PRIpaperSupplements.docx","url":"https://assets-eu.researchsquare.com/files/rs-5954459/v1/3750d250492470d5f29ddd5c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Responsible investment into the global mining industry – a spatial analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResponsible investment, namely the integration of Environmental, Social and Governance (ESG) factors into investment decisions, is a fast-growing practice in the finance sector. Founded in 2005, the Principles for Responsible Investment (PRI) now gather signatories that cover 50% of managed capital globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A Deloitte survey estimated that the practice of ESG integration has been adopted by 83% of investors\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, although a lower percentage (34%) admit to considering ESG factors regularly, showing a potential disconnect between commitments and actions. The practice is motivated by a consensus among investors that ESG performance and financial performance are positively interlinked. Several seminal studies\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e have found a causal relationship between ESG and financial performance, however, this relationship is dependent on the ESG topic being considered, the data used to measure this topic, and the focus geography\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, questions remain on whether and how responsible investors, aka PRI signatories, make decisions based on ESG data. What kind of decisions are made? What kind of data is being used? A lot about the practice remains hidden in in-house proprietary data systems and internal processes. There is evidence\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e that ESG integration is happening, and that it has an impact on investees, i.e. that responsible investing leads to positive sustainability outcomes across industry sectors. However, there are major data quality issues\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which limit decision accuracy and impact, and lead to certain ESG factors being better integrated than others\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Most of the growth in ESG integration has occurred over the past five years, and there remains substantial potential for improvement.\u003c/p\u003e \u003cp\u003eA major investor intervention occurred in the mining sector after the Brumadinho tailings dam failure that killed 272 people in 2019. The intervention had an immediate impact on the sector, leading to a new global standard on tailings management launched in 2020 and the creation, in 2022, of the Global Investor Commission on Mining (GICM) 2030. Mining is one of the lowest-performing and highest-risk sectors according to several ESG rating providers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. At the same time, the mining industry is also a supplier of minerals and metals needed in the energy transition, making it essential to global decarbonisation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In this context, the finance sector can exert a high influence in shaping ESG performance in mining, yet it must also navigate a complex and challenging landscape.\u003c/p\u003e \u003cp\u003eThis paper reverse-engineers investors' decision-making process to identify the factors they consider when investing in the mining sector. To achieve this, we analysed the investor records of a dataset of 37,434 geolocated mining properties owned by 9,843 mining companies\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. 7,583 properties (1,247 companies) are receiving investment from PRI signatories, and 3,466 (447 companies) from GICM 2030 supporters - a subset of PRI signatories actively involved in the mining sector. We applied a set of ESG metrics from S\u0026amp;P Global\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and the Metals and Mining Sustainability Accounting Standard\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, otherwise known as the SASB standard and principally used by investors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e (see Supplementary Table\u0026nbsp;1 for the list of topics and metrics in the SASB standard). The paper finds evidence that ESG factors may be influencing investment decisions, but questions whether these decisions are leading to positive sustainability outcomes across the sector.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eResponsible investors\u0026rsquo; stakes in the global mining sector\u003c/p\u003e\n\u003cp\u003eThe information gap around mining asset ownership is substantial. This is consistent with the literature documenting data gaps across the global mining sector\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Of 37,434 geolocated mining assets, 14,886 (40%) have no ownership record (i.e., the companies that own the assets) and no institutional investor record (i.e., the finance organisations that own the companies), and another 12,527 (33%) have an ownership record but no institutional investor record (Supplementary Figs.\u0026nbsp;1 \u0026amp; 2). These 12,527 assets are typically owned by private companies, governments, or public companies exclusively owned by individuals. The remaining 10,021 assets (27%) have investor records (Supplementary Fig.\u0026nbsp;3). A total of 3,247 different institutional investors are listed as having some equity in these assets, out of which 626 are PRI signatories, which corresponds to 12% of the 5,348 PRI signatories globally (as of March 2024).\u003c/p\u003e\n\u003cp\u003eOverall, 7,583 assets are partly owned by PRI investors (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, b), and 9,506 by non-PRI investors, with 7,068 assets owned by both types of institutional investors (and 2,953 owned exclusively by one type of investor). While non-PRI investors own 23% more assets, their ownership percentage is often lower than PRI investors (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). Taking these percentages into account, PRI investors ownership stake is 28% more than non-PRI investors, although there are almost four times more non-PRI investors than PRI investors in the dataset. This means a small proportion of PRI signatories are investing into the mining sector, but those that do invest heavily compared to other institutional investors. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed shows that 12 of the top 15 investors by number of assets are PRI signatories (See Supplementary Table\u0026nbsp;2 for the list of the top 100 largest institutional investors by mining portfolio size).\u003c/p\u003e\n\u003cp\u003e31 institutional investors, about 5% of PRI investors owning mining properties, are GICM 2030 supporters. Because they are fewer, these investors cumulatively own smaller stakes in mining properties compared to the whole PRI cohort (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). However, this small cohort is invested in a large number of properties \u0026ndash; 3,466, i.e. about half of PRI properties. Konwave is the GICM investor with the largest mining portfolio (1,294 assets), followed by Ninety One (757) and Qu\u0026eacute;bec\u0026rsquo;s Caisse de D\u0026eacute;p\u0026ocirc;t et Placement (716) (See Supplementary Table\u0026nbsp;3 for the list of GICM 2030 supporters owning mining properties). This indicates that GICM 2030 supporters have a particular interest in the mining sector compared to other PRI investors. However, GICM investors do not appear to be more active shareholders compared to other investors based on the PRI shareholder resolution database (PRI, 2024b). Proposing and voting resolutions are ways through which investors can influence companies on ESG matters. Only one resolution involving GICM investors was found: Ninety One and Rathbones voting against Glencore\u0026apos;s 2024 Climate Action Plan, judging it not ambitious enough. For further analysis of shareholder resolutions, see Supplementary Text 1.\u003c/p\u003e\n\u003cp\u003eGeographic distributions of PRI and non-PRI properties exhibit some notable differences in top mining countries. PRI investors are significantly less present in China, Russia, India, the Philippines, Mexico, and Kyrgyzstan (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). This could be reflective of geopolitical alliances and the current move of some major western economies to diversify mineral production away from China and Russia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This is also reflective of the general geography of the PRI movement, with only 3% of all PRI signatories headquartered in China, and only one signatory headquartered in Russia. Compared to other PRI investors, GICM investors are more present in Canada, Sweden and Finland, but also Russia, Papua New Guinea and Bolivia (Supplementary Fig.\u0026nbsp;4). They appear to avoid countries like China, India and the Democratic Republic of Congo to a larger extent than other PRI investors.\u003c/p\u003e\n\u003cp\u003eESG considerations in mining asset ownership\u003c/p\u003e\n\u003cp\u003eESG scores from S\u0026amp;P Global were extracted for the mining companies in our dataset. See Methods for how S\u0026amp;P ESG scores are built. Scores were available only for 358 (out of 9,843) mining companies, together owning 4,154 mining properties, i.e. 11% of the dataset. Analysis of this sample reveals that mining portfolios of PRI and GICM 2030 investors exhibit higher ESG score \u0026ndash; 13 and 18% higher, respectively \u0026ndash; than the global average. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea shows that companies owned more than 10% by PRI investors have noticeably higher ESG scores, and so do companies owned by GICM investors (all percentages included). Spearman rank correlation coefficients are 0.401 for PRI and 0.391 for GICM investors (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). In comparison, companies financed by non-PRI investors have ESG scores 5% higher than the global average.\u003c/p\u003e\n\u003cp\u003ePRI and GICM portfolios exhibit noteworthy specificities, with PRI investors displaying a preference for large mining companies while GICM investors have a more balanced portfolio. The GICM cohort is more invested than the PRI cohort in exploration projects (Supplementary Fig.\u0026nbsp;5), and the exploration sector being dominated by junior companies largely explains the difference between PRI and GICM portfolios. ESG scores are correlated to company size (measured in number of properties owned), as large companies have higher incentives (visibility and reputation) as well as capability and resources to improve their ESG performance\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. When discounting the size effect, correlations between PRI/GICM percentages and ESG scores are lower but remain strong \u0026ndash; with coefficients of 0.347 for PRI and 0.357 for GICM (Supplementary Table\u0026nbsp;4). These results indicate that PRI and GICM investors are considering ESG metrics in investment decisions, to a larger extent than non-PRI investors.\u003c/p\u003e\n\u003cp\u003eTo complement S\u0026amp;P ESG scores and expand the analysis to the full set of 37,434 mining properties and 9,843 mining companies, we created eight individual ESG metrics that were applicable to the entire dataset: coal properties, tailings management, water scarcity, corruption risk, protected areas, threatened species, Indigenous land, and conflict areas. Apart from tailings management, which measures a company\u0026rsquo;s commitment to implementing the Global Industry Standard on Tailings Management (GISTM) and is therefore a company-wide metric, all selected metrics are property-specific, meaning values vary across properties owned by the same company. They relate to either the characteristic of a property (e.g. whether the property extracts coal) or its location (e.g. whether a property is in a protected area). These eight metrics were selected to align with the ESG topics covered by the SASB standard. The SASB standard defines these topics as \u0026ldquo;sustainability-related risks and opportunities that could reasonably be expected to affect [a mining company\u0026rsquo;s] cash flows, its access to finance or cost of capital\u0026rdquo;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For this analysis, metrics were defined to measure a risk (e.g. absence of commitment to tailings management) rather than an opportunity (see Methods and Supplementary Table\u0026nbsp;1 for how metrics were calculated).\u003c/p\u003e\n\u003cp\u003eWe find that GICM investors avoid coal properties, with an investment proportion of 4% while coal properties make up 14% of the dataset. Conversely, both PRI and non-PRI investors invest in coal properties in proportions higher than the global average (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec,d). Results are statistically significant. This is a surprising finding as decarbonization is the ESG topic that has received the most attention from responsible investors, in the mining sector and elsewhere\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Ownership of coal properties has been the object of scrutiny from investors. BlackRock\u0026rsquo;s announcement that it will no longer invest in companies that generate more than 25% of their revenue from thermal coal production incentivised large mining companies like BHP, Rio Tinto and South32 to divest their coal properties\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Findings indicate that these publicized events, and the small cohort of GICM investors, may be the exception rather than the norm across the global mining sector. Noting, however, that the proportion of coal properties is only one decarbonization metric among others and it is possible that investors are acting in other ways.\u003c/p\u003e\n\u003cp\u003ePRI and GICM investors invest preferably in companies committed to implementing the GISTM. While GISTM-committed companies only make up 1% of all mining companies, they make up 20% of PRI and GICM investments (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec,d). Non-PRI investors also appear to consider this topic, but to a lesser extent. 10% of non-PRI investments target GISTM-committed companies. Tailings management gained visibility following the mining sector\u0026rsquo;s credibility crisis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, triggered by the 2019 catastrophic tailings dam failure in Brumadinho, Brazil. Shortly after the event, a coalition of 100 investors issued a call to action, pressing the mining sector to address this systemic safety issue and have monitored progress ever since. The Church of England Pension Board\u0026rsquo;s (the institution that led the Investor Mining and Tailings Safety Initiative) official list of GISTM-committed companies is publicly available on their website\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and represents an easy way for investors to filter out non-committed companies. Noting that most GISTM-committed companies are large and therefore company size may be a confounding variable.\u003c/p\u003e\n\u003cp\u003eCorruption appears to receive some consideration from PRI, non-PRI, and GICM investors. Statistical tests indicate non-PRI investors consider corruption risk the most (see Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec,d). While there is a 19% decrease in corruption risk in the weighted average for GICM investors, the trend is not statistically significant. Mining properties receive a corruption risk score based on the country in which they are located, and which corresponds to the country\u0026rsquo;s Corruption Perception Index\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e score (see Methods).\u003c/p\u003e\n\u003cp\u003eThe average proportion of properties located nearby a violent conflict is 16% and 19% lower in PRI and GICM portfolios respectively compared to the global average. However, logistic regression test only shows weak correlation for conflict, and the chi-square test returns no statistical significance. Corruption and conflict areas are both measures of a country\u0026rsquo;s governance, and the countries in which a company operates has long been a factor influencing credit ratings\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, i.e. a company\u0026apos;s creditworthiness and therefore its ability to repay its debt. Our results confirm that institutional investors generally recognise the financial implications of corruption risk for business. Conversely, local conflict may not be a broadly accepted governance measure.\u003c/p\u003e\n\u003cp\u003eThe two biodiversity metrics, threatened species and protected areas, appear to receive some consideration from investors. The GICM portfolio\u0026rsquo;s exposure to threatened species is 22% lower than the global average, meaning mining properties that receive GICM investment are less likely to be in areas with identified threatened species. The negative correlations between threatened species richness and GICM and non-PRI ownership are weak but significant. Conversely, GICM investors do not seem to consider protected areas in their portfolio decisions, while PRI and non-PRI investors do (and the relationship is statistically significant). A recent position statement from a group of major mining companies commits to \u0026ldquo;respecting legally designated protected areas\u0026rdquo;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Movement in this space appears to be reflected in investment flows; although the observed correlations may also be due to PRI and non-PRI investors preferring major mining companies, regardless of their biodiversity commitments.\u003c/p\u003e\n\u003cp\u003eFinally, GICM investors\u0026rsquo; portfolios appear to be exposed to levels of water scarcity 22% lower than the global average. This difference is significant according to the logistic regression test, but not the chi-square test. Analysis of mining properties\u0026rsquo; overlap with Indigenous land reveals no statistical relationship, indicating that investors do not consider this topic.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOverall, the analysis yielded two key insights. Firstly, responsible investors consider ESG metrics in their investment decisions. The comparison of PRI and non-PRI portfolios suggests significant differences in practices between the two investor cohorts. PRI signatories state that their primary motivations for joining the PRI are \u0026ldquo;long-term value\u0026rdquo; and \u0026ldquo;risk management\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, indicating that these investors see ESG as financially material and are acting accordingly. It should be noted that the Principles for Responsible Investment are not a constraining framework and allow signatories to exercise their own judgement over which ESG topics are important to them\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSecondly, PRI signatories have a significant presence across the global mining sector. This contrasts with stated concerns about the sector being systematically underweighted and screened out by responsible investors due to its poor ESG credentials\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Negative screening, a practice where investors exclude or divest from companies or assets that do not meet certain ESG criteria, has been shown to be counterproductive, as it fails to encourage improvement in the performance of the targeted firms\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. A potential reason why we did not find evidence of negative screening could be that large investors, who offer a variety of funds to their clients, exclude mining companies from their \u0026lsquo;sustainable\u0026rsquo; funds (funds that consider ESG criteria) but include them in their \u0026lsquo;traditional\u0026rsquo; funds. This may also explain why we did not observe a screen-out of coal properties in the PRI cohort, at the exception of GICM investors.\u003c/p\u003e \u003cp\u003eThe fact that PRI and GICM investors are involved in the mining sector and see the importance of ESG in their investment decisions is positive news. Through their financial stakes in companies and influence on boards of directors, investors can be critical agents of change\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The simple growth of the PRI initiative sends a signal to companies that encourages them to improve their ESG performance\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite this, it is not clear whether current investment strategies do result in tangible improvement in social and environmental outcomes on the ground. Our analysis does not allow reaching a conclusion on this matter. However, we can provide initial reflections based on available information and literature.\u003c/p\u003e \u003cp\u003eOur analysis suggests PRI and GICM investors rely on aggregated ESG scores. This is positive and yet problematic as these scores are widely acknowledged, even amongst the investment community, as being poor performance measures\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. One reason is that a single score is too simplistic and hides variations in performance across topics. Another reason is that ESG scores typically measure commitments rather than actions, and the presence of corporate policies rather than their effective implementation on the ground. The strong correlations found between PRI investment and a company\u0026rsquo;s commitment to the global tailings standard is an example of a focus on pledges rather than performance. While standards are needed components of corporate governance, there is a risk that investors only engage superficially with the mining sector and that this does not result in meaningful social and environmental outcomes in extractive locations. Responsible investors may be mainly applying a one-size-fits-all approach, rather than a specialised approach that recognises the diversity of ESG topics and operating contexts. The analysis of shareholder resolutions (Supplementary Text 1) suggests that specialised practices, such as active stewardship where investors directly engage with boards of directors on ESG matters, are rare\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. One nuance to highlight, however, is the observed trend in the GICM cohort, who exhibits a more sophisticated investment approach that considers some situated risks (i.e. risks based on a property\u0026rsquo;s location), particularly water scarcity and threatened species.\u003c/p\u003e \u003cp\u003eOverall, our findings align with responsible investment studies outside the mining sector. These studies have found that ESG integration have limited impact on social and environmental outcomes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, meaning that responsible investment may not have delivered on its promises. The Cambridge Institute for Sustainability Leadership finds that ESG and the boom in responsible funds have not only failed to deliver but have been counter-productive at times as they give the impression of progress while having \u0026ldquo;no realistic prospect of doing enough\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. A significant proportion of investors genuinely aim to invest in support of sustainability objectives, but do not have appropriate mandates, frameworks, tools, or data, to do so effectively. Research efforts need to be dedicated to supporting responsible investors in their practice. Furthermore, there is a need for mining companies to drive improvement in investor relations, by communicating more meaningfully about their ESG performance and building investor support on the ESG initiatives that truly result in positive outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eOverall data quality.\u003c/b\u003e The S\u0026amp;P Capital IQ Pro database forms the basis of nearly all global assessments published on the mining sector\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Its geolocated mining property dataset provides a rich source of information for spatial analysis. However, this database is incomplete and sometimes inaccurate. Data quality issues are also found in ESG datasets. While this is a general limitation, inaccuracies linked to property locations and ESG metrics are not expected to affect the main findings and conclusions of the study. The study\u0026rsquo;s focus is on understanding how investment decisions are made and with what data, not on identifying what ESG risks are indeed present in a given mining location. The study assumes that investors have access to the same (or similar) incomplete and inaccurate data and make decisions based on this data. However, other data quality issues may affect the results and are noted in the rest of this section, as well as in the main text where appropriate.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOwnership information.\u003c/b\u003e Ownership records were extracted for the 37,434 mining properties listed in the S\u0026amp;P database. Removal of duplicates returned a list of 9,843 mining companies (public or private). For these companies, institutional investor equity percentages were extracted for the 30 largest investors by equity percentage. The S\u0026amp;P database\u0026rsquo;s screener function does not allow extraction of more than 30 investors. This limitation affects 473 companies that have more than 30 investors. Removal of duplicates returned a list of 3,247 institutional investors with stakes in mining companies.\u003c/p\u003e \u003cp\u003eThe S\u0026amp;P list of investors was matched against the PRI list of 5,348 signatories and the GICM 2030 list of 82 supporters. This step required manual checks as investor names in the S\u0026amp;P database were often different from names in the PRI and GICM lists. For instance, \u0026ldquo;Brookfield Corp.\u0026rdquo; in the S\u0026amp;P database was matched with \u0026ldquo;Brookfield Corporation\u0026rdquo; in the PRI list. When an investor from the S\u0026amp;P list was a 100% subsidiary of a larger PRI signatory, this investor was identified as a PRI signatory, while the reverse was not. This results in a conservative list of PRI investors. Because of time constraints, not all 3,247 investors could be manually checked. Investors that had two or fewer mining companies, five or fewer mining assets, and who\u0026rsquo;s equity position was 11th or lower (i.e. the investor was not in the top 10 in equity percentage), were not checked. This resulted in a small number of investors being left out. Overall, 92% of investors were identified as being either \u0026ldquo;PRI\u0026rdquo; or \u0026ldquo;non-PRI\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cb\u003eS\u0026amp;P Global ESG scores.\u003c/b\u003e These scores, calculated by S\u0026amp;P for certain companies, measure companies\u0026rsquo; performance on, and management of, material ESG issues. An issue is considered material if it presents a significant risk, opportunity or impact on i) society or the environment, and ii) on a company\u0026rsquo;s long-term financial performance. The scores rely on data collected from corporate disclosures, media and other public stakeholder information. They are measured on a scale of 0\u0026ndash;100 where 100 represents the best performance. The full methodology for building the ESG score is publicly available\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, but the scores themselves are available via subscription.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndividual ESG risk metrics.\u003c/b\u003e Except for tailings management, which is a company-level metric, individual ESG metrics are calculated at the property-level. Six of the eight individual ESG metrics, namely protected areas, coal properties, tailings management, water scarcity, conflict areas and Indigenous land, are categorical and binary, i.e. they represent the presence or absence of a condition or criterion (for instance, a property is either nearby a conflict area or not; a company is either GISTM-committed or not). For these metrics, mining properties are thus either attributed a 1 or a 0, where 1 corresponds to the presence of risk. The remaining two metrics, corruption and threatened species, are continuous metrics and are normalised for properties to receive a value between 0 and 1, where 1 corresponds to the highest level of risk. Metrics are defined as follows:\u003c/p\u003e \u003cp\u003e(1) The tailings management metric records when a company is not listed as being GISTM-committed. The list of committed companies is available on the Church of England Pension Board\u0026rsquo;s website \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(2) The coal properties metric records whether a property\u0026rsquo;s primary targeted commodity is coal. Such a property receives a value of 1, while others receive a value of 0. Commodity information at the property level is sourced from the S\u0026amp;P Global database\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(3) The corruption metric is calculated using Transparency International\u0026rsquo;s Corruption Perceptions Index\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Properties located in a particular country are assigned the index value of that country. Corruption Perception Index values were inversed so that higher corruption risks were associated with higher metric scores.\u003c/p\u003e \u003cp\u003e(4) The conflict areas metric measures whether a mining property is located nearby (less than 10 km) a violent conflict using the Uppsala Conflict Data Program\u0026rsquo;s Georeferenced Event Dataset\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(5) Water scarcity uses the World Resources Institute\u0026rsquo;s Aqueduct Water Risk Framework\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. It overlays mining property locations with the Aqueduct\u0026rsquo;s Baseline Water Stress map and assesses whether a property is in an area of high or very high water stress.\u003c/p\u003e \u003cp\u003e(6) The threatened species metric uses the International Union for Conservation of Nature\u0026rsquo;s Red List of Threatened Species\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and overlays property locations with species locations.\u003c/p\u003e \u003cp\u003e(7) The protected areas metric uses the International Union for Conservation of Nature\u0026rsquo;s World Database of Protected Areas\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and analyses whether a property falls in or nearby (less than 5 km) a protected area.\u003c/p\u003e \u003cp\u003e(8) The Indigenous land metric uses Garnett et al.\u0026rsquo;s\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Indigenous Peoples land map and analyses whether a property falls in or nearby (less than 5 km) Indigenous Peoples land.\u003c/p\u003e \u003cp\u003eIndividual metrics were selected to align with the SASB standard on mining and metals (see Supplementary Table\u0026nbsp;1). SASB standards are curated by the IFRS Foundation since 2001 and are required for use by more than 140 jurisdictions\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. SASB standards are designed to streamline the disclosure and collection of comparable and standardised data tailored for investors to make informed decisions. The IFRS states that \u0026ldquo;because they are industry-based, metric-driven and focused on the risks and opportunities most likely to affect cash flows, access to finance and cost of capital, SASB Standards enable integration of sustainability considerations into investment and stewardship decisions across global portfolios and asset classes\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The PRI association promotes the use of the SASB standards\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt should be noted that the level of alignment between the metrics used in the study and the standard varies across metrics (Supplementary Table\u0026nbsp;1). Corruption risk is the metric most closely aligned, as the standard explicitly mentions the Corruption Perception Index. The threatened species and protected area metrics are also fairly aligned, as the datasets used to measure these factors are widely recognised as the main points of reference for global assessments. This is not the case for water scarcity and conflict areas, for which several reputable datasets exist, and for Indigenous land, where the only global spatial dataset available is not in open access. Furthermore, maps of Indigenous land occupation or management are often contentious and subject to definitional issues\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Tailings management and coal properties are the least aligned metrics as they overly simplify the content of the standard. However, they represent meaningful proxies. Differences in alignment may influence some of our findings.\u003c/p\u003e \u003cp\u003eFinally, a general source of misalignment is that SASB standard typically requires to measure the percentage of production or the percentage of reserves e.g. in high corruption-risk countries, or in protected areas, whereas our study measures percentage of properties. We do not rely on production or reserves data for this study due to their incompleteness and inaccuracy. Using this data would risk compounding inaccuracies and would result in the sample size being considerably reduced, as it would exclude many properties that do not have production or reserves data.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWeighted average relative difference.\u003c/b\u003e For each individual ESG metric, this value is calculated using the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{{A}_{x}-B}{({A}_{x}+B)/2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{x}\\)\u003c/span\u003e\u003c/span\u003e is the weighted average for investor type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e :\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{A}_{x}=\\:\\frac{\\sum\\:_{i=1}^{n}{ESG}_{i}\\times\\:{Ownership\\%}_{i,x}}{{{\\sum\\:}_{i=1}^{n}Ownership\\%}_{i,x}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnd \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:B\\)\u003c/span\u003e\u003c/span\u003e is the global average of the ESG metric:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:B=\\frac{{\\sum\\:}_{i=1}^{n}{ESG}_{i}}{n}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ESG}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the metric value at mining property \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Ownership\\%}_{i,x}\\)\u003c/span\u003e\u003c/span\u003e is the aggregated ownership percentage of investor type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e at mining property \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor tailings management, which is calculated at the company level, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=\\:\\text{9,843}\\)\u003c/span\u003e\u003c/span\u003e, i.e. the total number of mining companies. For coal properties, protected areas, conflict areas, and Indigenous land, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=\\text{37,434}\\)\u003c/span\u003e\u003c/span\u003e, i.e. the total number of mining properties. For corruption risk, water scarcity and threatened species, a small number of properties fall in areas of \u0026ldquo;no data\u0026rdquo; (98, 286, and 757, respectively) and were therefore removed from the calculation. Hence, for these three metrics, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\u0026lt;\\text{37,434}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical tests.\u003c/b\u003e Statistical tests were undertaken to evaluate the robustness of the results. See Supplementary Tables\u0026nbsp;5\u0026ndash;8 for all test results. For categorical metrics (protected areas, coal properties, tailings management, water scarcity, conflict areas and Indigenous land), the first step was to use the \u0026ldquo;chi-square goodness of fit test\u0026rdquo; to test whether the relative occurrence of an ESG risk at properties owned by a certain investor type (regardless of the ownership percentage) was statistically different to the general occurrence based on p-values. The second step was to see whether occurrence of a certain ESG factor was related to the percentage of ownership of different investor types using logistic regression, where the parameter value indicates the direction and magnitude of the relationship while the parameter p-value measures statistical significance. For instance, logistic regression tests whether the likelihood of a coal asset occurring is related to the PRI ownership percentage.\u003c/p\u003e \u003cp\u003eSeparate tests were designed for continuous metrics, i.e. threatened species and corruption. The first test was to check whether the frequency distribution of these metrics at properties owned by certain investor types (regardless of the ownership percentage) was significantly different from their distribution across all mining properties. Assuming that institutional investors would prefer mining properties with lower ESG risks, an \u0026ldquo;Unpaired Mann-Whitney-Wilcoxon U test\u0026rdquo; was used to test whether the distribution of properties owned by a particular investor type was significantly lower than that of all mining properties. This test serves a similar purpose to chi-squared test but for continuous data. Results are expressed in terms of p-values (in Supplementary Table\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eThe second test was to see whether the value of a certain ESG metric was related to percentage of ownership of a particular investor type using Spearman correlations, whereby p-values were derived with t-tests. Spearman correlations were used because of the strong non-normality of ESG metrics and ownership percentage distributions. This test was performed for two property cohorts: one that contained all mining properties, and one that contained only mining properties that had some ownership by the specific institutional investor type being considered (e.g. for PRI investors, only properties with a non-zero PRI ownership percentage were considered). Due to the high number of properties with 0% ownership by a specific investor type, this second assessment was included to better understand the distribution of investor funds in selected properties as opposed to all properties.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePRI. PRI Strategy Plan 2024-27. (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeloitte (2024) Investor trust in sustainability data - An opportunity for corporate leaders\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdmans A (2012) The Link Between Job Satisfaction and Firm Value, With Implications for Corporate Social Responsibility. 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PRI supports progress on IFRS Sustainability Standards (2023)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5954459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5954459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResponsible investment, namely the integration of Environmental, Social and Governance data into investment decisions, is now a mainstream practice in the finance sector. However, questions remain on how investors make decisions based on this type of data and whether these decisions lead to improvements in social and environmental outcomes on the ground. This paper analyses investment into the global mining sector through the institutional ownership records of a global dataset of 37,434 mining properties and 9,843 mining companies. The study reveals that 626 \u0026ldquo;responsible\u0026rdquo; investors, namely institutional investors that signed the Principles for Responsible Investment, own stakes in 1,247 mining companies and 7,583 mining properties. These investors prefer to invest in companies with higher Environmental, Social and Governance performance scores, with an average score 13% higher than the industry average. Tailings governance, corruption risk, and proximity to protected areas receive consideration from responsible investors, while water scarcity and proximity to Indigenous land do not. Contrary to expectations, responsible investors invest 23% more in coal mining properties than the global average. We surmise that current investment approaches are unlikely to contribute to significant on-the-ground improvement in practice.\u003c/p\u003e","manuscriptTitle":"Responsible investment into the global mining industry – a spatial analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 12:00:25","doi":"10.21203/rs.3.rs-5954459/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9bbf273c-7850-4110-afe3-3a700fbf344c","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":44164548,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"},{"id":44164549,"name":"Scientific community and society/Business and industry/Business"},{"id":44164550,"name":"Scientific community and society/Geography"}],"tags":[],"updatedAt":"2025-03-28T12:00:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-28 12:00:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5954459","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5954459","identity":"rs-5954459","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}